





import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin

class   A							(						__lowercase		,   unittest.TestCase    ):
      _snake_case				  =CanineTokenizer
      _snake_case				  =False



      def 						lowerCAmelCase__     (						self:    Optional[Any]							)    ->     List[str]:
         '''simple docstring'''

         super().setUp()
         UpperCAmelCase_           =CanineTokenizer()
         tokenizer.save_pretrained(self.tmpdirname							)



      @cached_property
      def 						lowerCAmelCase__     (						self:    Optional[int]							)    ->     List[str]:
         '''simple docstring'''

         return CanineTokenizer.from_pretrained("google/canine-s"							)



      def 						lowerCAmelCase__     (						self:    Union[str, Any]		,	**_lowerCAmelCase:    List[Any]							)    ->     CanineTokenizer:
         '''simple docstring'''

         UpperCAmelCase_           =self.tokenizer_class.from_pretrained(self.tmpdirname		,	**_lowerCAmelCase							)
         UpperCAmelCase_           =1024
         return tokenizer



      @require_torch
      def 						lowerCAmelCase__     (						self:    int							)    ->     Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_           =self.canine_tokenizer
         UpperCAmelCase_           =["Life is like a box of chocolates.", "You never know what you're gonna get."]
         # fmt: off
         UpperCAmelCase_           =[5_7344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 5_7345, 0, 0, 0, 0]
         # fmt: on
         UpperCAmelCase_           =tokenizer(_lowerCAmelCase		,	padding=_lowerCAmelCase		,	return_tensors="pt"							)
         self.assertIsInstance(_lowerCAmelCase		,	_lowerCAmelCase							)

         UpperCAmelCase_           =list(batch.input_ids.numpy()[0]							)

         self.assertListEqual(_lowerCAmelCase		,	_lowerCAmelCase							)

         self.assertEqual((2, 39)		,	batch.input_ids.shape							)
         self.assertEqual((2, 39)		,	batch.attention_mask.shape							)



      @require_torch
      def 						lowerCAmelCase__     (						self:    int							)    ->     str:
         '''simple docstring'''

         UpperCAmelCase_           =self.canine_tokenizer
         UpperCAmelCase_           =["Once there was a man.", "He wrote a test in HuggingFace Tranformers."]
         UpperCAmelCase_           =tokenizer(_lowerCAmelCase		,	padding=_lowerCAmelCase		,	return_tensors="pt"							)
         # check if input_ids, attention_mask and token_type_ids are returned
         self.assertIn("input_ids"		,	_lowerCAmelCase							)
         self.assertIn("attention_mask"		,	_lowerCAmelCase							)
         self.assertIn("token_type_ids"		,	_lowerCAmelCase							)



      @require_torch
      def 						lowerCAmelCase__     (						self:    str							)    ->     Any:
         '''simple docstring'''

         UpperCAmelCase_           =self.canine_tokenizer
         UpperCAmelCase_           =[
             "What's the weater?",
             "It's about 25 degrees.",
         ]
         UpperCAmelCase_           =tokenizer(
             text_target=_lowerCAmelCase		,	max_length=32		,	padding="max_length"		,	truncation=_lowerCAmelCase		,	return_tensors="pt"							)
         self.assertEqual(32		,	targets["input_ids"].shape[1]							)



      def 						lowerCAmelCase__     (						self:    Optional[int]							)    ->     Tuple:
         '''simple docstring'''

         UpperCAmelCase_           =self.get_tokenizers()
         for tokenizer in tokenizers:
            with self.subTest(F'{tokenizer.__class__.__name__}'							):
               self.assertNotEqual(tokenizer.model_max_length		,	42							)

        # Now let's start the test
         UpperCAmelCase_           =self.get_tokenizers()
         for tokenizer in tokenizers:
            with self.subTest(F'{tokenizer.__class__.__name__}'							):
               # Isolate this from the other tests because we save additional tokens/etc
               UpperCAmelCase_           =tempfile.mkdtemp()

               UpperCAmelCase_           =" He is very happy, UNwant\u00E9d,running"
               UpperCAmelCase_           =tokenizer.encode(_lowerCAmelCase		,	add_special_tokens=_lowerCAmelCase							)
               tokenizer.save_pretrained(_lowerCAmelCase							)

               UpperCAmelCase_           =tokenizer.__class__.from_pretrained(_lowerCAmelCase							)
               UpperCAmelCase_           =after_tokenizer.encode(_lowerCAmelCase		,	add_special_tokens=_lowerCAmelCase							)
               self.assertListEqual(_lowerCAmelCase		,	_lowerCAmelCase							)

               shutil.rmtree(_lowerCAmelCase							)

         UpperCAmelCase_           =self.get_tokenizers(model_max_length=42							)
         for tokenizer in tokenizers:
            with self.subTest(F'{tokenizer.__class__.__name__}'							):
               # Isolate this from the other tests because we save additional tokens/etc
               UpperCAmelCase_           =tempfile.mkdtemp()

               UpperCAmelCase_           =" He is very happy, UNwant\u00E9d,running"

               UpperCAmelCase_           =tokenizer.additional_special_tokens

               # We can add a new special token for Canine as follows:
               UpperCAmelCase_           =chr(0xe0_07							)
               additional_special_tokens.append(_lowerCAmelCase							)
               tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}							)
               UpperCAmelCase_           =tokenizer.encode(_lowerCAmelCase		,	add_special_tokens=_lowerCAmelCase							)
               tokenizer.save_pretrained(_lowerCAmelCase							)

               UpperCAmelCase_           =tokenizer.__class__.from_pretrained(_lowerCAmelCase							)
               UpperCAmelCase_           =after_tokenizer.encode(_lowerCAmelCase		,	add_special_tokens=_lowerCAmelCase							)
               self.assertListEqual(_lowerCAmelCase		,	_lowerCAmelCase							)
               self.assertIn(_lowerCAmelCase		,	after_tokenizer.additional_special_tokens							)
               self.assertEqual(after_tokenizer.model_max_length		,	42							)

               UpperCAmelCase_           =tokenizer.__class__.from_pretrained(_lowerCAmelCase		,	model_max_length=43							)
               self.assertEqual(tokenizer.model_max_length		,	43							)

               shutil.rmtree(_lowerCAmelCase							)



      def 						lowerCAmelCase__     (						self:    int							)    ->     Tuple:
         '''simple docstring'''

         UpperCAmelCase_           =self.get_tokenizers(do_lower_case=_lowerCAmelCase							)
         for tokenizer in tokenizers:
            with self.subTest(F'{tokenizer.__class__.__name__}'							):
               UpperCAmelCase_    ,							UpperCAmelCase_           =self.get_clean_sequence(_lowerCAmelCase							)

               # a special token for Canine can be defined as follows:
               UpperCAmelCase_           =0xe0_05
               UpperCAmelCase_           =chr(_lowerCAmelCase							)

               tokenizer.add_special_tokens({"cls_token": special_token}							)
               UpperCAmelCase_           =tokenizer.encode(_lowerCAmelCase		,	add_special_tokens=_lowerCAmelCase							)
               self.assertEqual(len(_lowerCAmelCase							)		,	1							)

               UpperCAmelCase_           =tokenizer.decode(ids + encoded_special_token		,	clean_up_tokenization_spaces=_lowerCAmelCase							)
               UpperCAmelCase_           =tokenizer.encode(_lowerCAmelCase		,	add_special_tokens=_lowerCAmelCase							)

               UpperCAmelCase_           =tokenizer.encode(_lowerCAmelCase		,	add_special_tokens=_lowerCAmelCase							)
               UpperCAmelCase_           =tokenizer.encode(_lowerCAmelCase		,	add_special_tokens=_lowerCAmelCase							)
               self.assertEqual(_lowerCAmelCase		,	input_encoded + special_token_id							)

               UpperCAmelCase_           =tokenizer.decode(_lowerCAmelCase		,	skip_special_tokens=_lowerCAmelCase							)
               self.assertTrue(special_token not in decoded							)



      def 						lowerCAmelCase__     (						self:    Any							)    ->     Any:
         '''simple docstring'''

         UpperCAmelCase_           =self.get_tokenizers(do_lower_case=_lowerCAmelCase							)
         for tokenizer in tokenizers:
            with self.subTest(F'{tokenizer.__class__.__name__}'							):
               UpperCAmelCase_           =chr(0xe0_05							)
               UpperCAmelCase_           =chr(0xe0_06							)

               # `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
               tokenizer.add_tokens([SPECIAL_TOKEN_1]		,	special_tokens=_lowerCAmelCase							)
               # `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
               # which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
               tokenizer.add_special_tokens({"additional_special_tokens": [SPECIAL_TOKEN_2]}							)

               UpperCAmelCase_           =tokenizer.tokenize(_lowerCAmelCase							)
               UpperCAmelCase_           =tokenizer.tokenize(_lowerCAmelCase							)

               self.assertEqual(len(_lowerCAmelCase							)		,	1							)
               self.assertEqual(len(_lowerCAmelCase							)		,	1							)
               self.assertEqual(token_a[0]		,	_lowerCAmelCase							)
               self.assertEqual(token_a[0]		,	_lowerCAmelCase							)



      @require_tokenizers
      def 						lowerCAmelCase__     (						self:    Optional[Any]							)    ->     Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_           =self.get_tokenizers(do_lower_case=_lowerCAmelCase							)
         for tokenizer in tokenizers:
            with self.subTest(F'{tokenizer.__class__.__name__}'							):
               # a special token for Canine can be defined as follows:
               UpperCAmelCase_           =0xe0_06
               UpperCAmelCase_           =chr(_lowerCAmelCase							)

               UpperCAmelCase_           =AddedToken(_lowerCAmelCase		,	lstrip=_lowerCAmelCase							)
               tokenizer.add_special_tokens({"additional_special_tokens": [new_token]}							)

               with tempfile.TemporaryDirectory() as tmp_dir_name:
                  tokenizer.save_pretrained(_lowerCAmelCase							)
                  tokenizer.from_pretrained(_lowerCAmelCase							)



      def 						lowerCAmelCase__     (						self:    Any							)    ->     List[Any]:
         '''simple docstring'''

         UpperCAmelCase_           =[]
         if self.test_slow_tokenizer:
            tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())							)

         if self.test_rust_tokenizer:
            tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())							)

         for tokenizer_class, tokenizer_utils in tokenizer_list:
            with tempfile.TemporaryDirectory() as tmp_dir:
               tokenizer_utils.save_pretrained(_lowerCAmelCase							)

               with open(os.path.join(_lowerCAmelCase		,	"special_tokens_map.json"							)		,	encoding="utf-8"							) as json_file:
                  UpperCAmelCase_           =json.load(_lowerCAmelCase							)

               with open(os.path.join(_lowerCAmelCase		,	"tokenizer_config.json"							)		,	encoding="utf-8"							) as json_file:
                  UpperCAmelCase_           =json.load(_lowerCAmelCase							)

               # a special token for Canine can be defined as follows:
               UpperCAmelCase_           =0xe0_06
               UpperCAmelCase_           =chr(_lowerCAmelCase							)

               UpperCAmelCase_           =[new_token_a]
               UpperCAmelCase_           =[new_token_a]

               with open(os.path.join(_lowerCAmelCase		,	"special_tokens_map.json"							)		,	"w"		,	encoding="utf-8"							) as outfile:
                  json.dump(_lowerCAmelCase		,	_lowerCAmelCase							)
               with open(os.path.join(_lowerCAmelCase		,	"tokenizer_config.json"							)		,	"w"		,	encoding="utf-8"							) as outfile:
                  json.dump(_lowerCAmelCase		,	_lowerCAmelCase							)

               # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
               # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
               # "special_tokens_map.json" files
               UpperCAmelCase_           =tokenizer_class.from_pretrained(_lowerCAmelCase		,	extra_ids=0							)
               self.assertIn(_lowerCAmelCase		,	tokenizer_without_change_in_init.additional_special_tokens							)
               # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
               self.assertEqual(
                   [new_token_a]		,	tokenizer_without_change_in_init.convert_ids_to_tokens(
                       tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a]							)							)		,	)

               UpperCAmelCase_           =0xe0_07
               UpperCAmelCase_           =chr(_lowerCAmelCase							)
               # Now we test that we can change the value of additional_special_tokens in the from_pretrained
               UpperCAmelCase_           =[AddedToken(_lowerCAmelCase		,	lstrip=_lowerCAmelCase							)]
               UpperCAmelCase_           =tokenizer_class.from_pretrained(
                   _lowerCAmelCase		,	additional_special_tokens=_lowerCAmelCase		,	extra_ids=0							)

               self.assertIn(_lowerCAmelCase		,	tokenizer.additional_special_tokens							)
               # self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
               self.assertEqual(
                   [new_token_a]		,	tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a]							)							)							)



      @require_tokenizers
      def 						lowerCAmelCase__     (						self:    Optional[Any]							)    ->     Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_           =self.get_tokenizers(do_lower_case=_lowerCAmelCase							)
         for tokenizer in tokenizers:
            with self.subTest(F'{tokenizer.__class__.__name__}'							):
               UpperCAmelCase_           ="hello world"
               if self.space_between_special_tokens:
                  UpperCAmelCase_           ="[CLS] hello world [SEP]"
               else:
                  UpperCAmelCase_           =input
               UpperCAmelCase_           =tokenizer.encode(_lowerCAmelCase		,	add_special_tokens=_lowerCAmelCase							)
               UpperCAmelCase_           =tokenizer.decode(_lowerCAmelCase		,	spaces_between_special_tokens=self.space_between_special_tokens							)
               self.assertIn(_lowerCAmelCase		,	[output, output.lower()]							)



      def 						lowerCAmelCase__     (						self:    List[str]							)    ->     Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_           =self.get_tokenizers()
         for tokenizer in tokenizers:
            with self.subTest(F'{tokenizer.__class__.__name__}'							):
               UpperCAmelCase_           =[
                   "bos_token",
                   "eos_token",
                   "unk_token",
                   "sep_token",
                   "pad_token",
                   "cls_token",
                   "mask_token",
               ]

         UpperCAmelCase_           ="a"
         UpperCAmelCase_           =ord(_lowerCAmelCase							)

         for attr in attributes_list:
            setattr(_lowerCAmelCase		,	attr + "_id"		,	_lowerCAmelCase							)
            self.assertEqual(getattr(_lowerCAmelCase		,	_lowerCAmelCase							)		,	_lowerCAmelCase							)
            self.assertEqual(getattr(_lowerCAmelCase		,	attr + "_id"							)		,	_lowerCAmelCase							)

            setattr(_lowerCAmelCase		,	attr + "_id"		,	_lowerCAmelCase							)
            self.assertEqual(getattr(_lowerCAmelCase		,	_lowerCAmelCase							)		,	_lowerCAmelCase							)
            self.assertEqual(getattr(_lowerCAmelCase		,	attr + "_id"							)		,	_lowerCAmelCase							)

         setattr(_lowerCAmelCase		,	"additional_special_tokens_ids"		,	[]							)
         self.assertListEqual(getattr(_lowerCAmelCase		,	"additional_special_tokens"							)		,	[]							)
         self.assertListEqual(getattr(_lowerCAmelCase		,	"additional_special_tokens_ids"							)		,	[]							)

         UpperCAmelCase_           =0xe0_06
         UpperCAmelCase_           =chr(_lowerCAmelCase							)
         setattr(_lowerCAmelCase		,	"additional_special_tokens_ids"		,	[additional_special_token_id]							)
         self.assertListEqual(getattr(_lowerCAmelCase		,	"additional_special_tokens"							)		,	[additional_special_token]							)
         self.assertListEqual(getattr(_lowerCAmelCase		,	"additional_special_tokens_ids"							)		,	[additional_special_token_id]							)



      def 						lowerCAmelCase__     (						self:    List[str]							)    ->     List[str]:
         '''simple docstring'''

         pass



      def 						lowerCAmelCase__     (						self:    Dict							)    ->     List[str]:
         '''simple docstring'''

         pass



      def 						lowerCAmelCase__     (						self:    Union[str, Any]							)    ->     Dict:
         '''simple docstring'''

         pass



      def 						lowerCAmelCase__     (						self:    Optional[Any]							)    ->     Union[str, Any]:
         '''simple docstring'''

         pass



      def 						lowerCAmelCase__     (						self:    Any							)    ->     List[Any]:
         '''simple docstring'''

         pass



      def 						lowerCAmelCase__     (						self:    List[Any]							)    ->     List[str]:
         '''simple docstring'''

         pass



      def 						lowerCAmelCase__     (						self:    Tuple							)    ->     Union[str, Any]:
         '''simple docstring'''

         pass






      def 						lowerCAmelCase__     (						self:    str							)    ->     str:
         '''simple docstring'''

         pass











'''simple docstring'''






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
					import os

					# The slow tests are often failing with OOM error on GPU
					# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
					# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
					lowerCAmelCase									=   """platform"""

					import jax
					import jax.numpy as jnp

					from transformers import BlenderbotTokenizer
					from transformers.models.blenderbot.modeling_flax_blenderbot import (
					    FlaxBlenderbotForConditionalGeneration,
					    FlaxBlenderbotModel,
					    shift_tokens_right,
					)






def   __A    (				a_    :	Dict   ,a_    :	List[Any]   ,a_    :	List[str]=None   ,a_    :	Optional[int]=None   ,a_    :	Any=None   ,a_    :	Any=None   ,a_    :	str=None   ,a_    :	Union[str, Any]=None   ,):
		if attention_mask is None:
				lowerCAmelCase  :     List[Any]       						= np.where(input_ids != config.pad_token_id   ,1   ,0						)
		if decoder_attention_mask is None:
				lowerCAmelCase  :     Union[str, Any]       						= np.where(decoder_input_ids != config.pad_token_id   ,1   ,0						)
		if head_mask is None:
				lowerCAmelCase  :     Dict       						= np.ones((config.encoder_layers, config.encoder_attention_heads)						)
		if decoder_head_mask is None:
				lowerCAmelCase  :     int       						= np.ones((config.decoder_layers, config.decoder_attention_heads)						)
		if cross_attn_head_mask is None:
				lowerCAmelCase  :     Union[str, Any]       						= np.ones((config.decoder_layers, config.decoder_attention_heads)						)
		return {
		    "input_ids": input_ids,
		    "decoder_input_ids": decoder_input_ids,
		    "attention_mask": attention_mask,
		    "decoder_attention_mask": attention_mask,
		}
class 		lowerCamelCase	:



	def __init__( self		,   a_		,   a_=13		,   a_=7		,   a_=True		,   a_=False		,   a_=99		,   a_=16		,   a_=2		,   a_=4		,   a_=4		,   a_="gelu"		,   a_=0.1		,   a_=0.1		,   a_=32		,   a_=2		,   a_=1		,   a_=0		,   a_=0.02		,   ):
			lowerCAmelCase  :     Optional[int]       						= parent
			lowerCAmelCase  :     Tuple       						= batch_size
			lowerCAmelCase  :     Any       						= seq_length
			lowerCAmelCase  :     int       						= is_training
			lowerCAmelCase  :     int       						= use_labels
			lowerCAmelCase  :     Any       						= vocab_size
			lowerCAmelCase  :     Tuple       						= hidden_size
			lowerCAmelCase  :     Optional[int]       						= num_hidden_layers
			lowerCAmelCase  :     int       						= num_attention_heads
			lowerCAmelCase  :     int       						= intermediate_size
			lowerCAmelCase  :     str       						= hidden_act
			lowerCAmelCase  :     List[Any]       						= hidden_dropout_prob
			lowerCAmelCase  :     List[str]       						= attention_probs_dropout_prob
			lowerCAmelCase  :     Dict       						= max_position_embeddings
			lowerCAmelCase  :     Optional[int]       						= eos_token_id
			lowerCAmelCase  :     Union[str, Any]       						= pad_token_id
			lowerCAmelCase  :     Tuple       						= bos_token_id
			lowerCAmelCase  :     Tuple       						= initializer_range



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Optional[Any]       						= np.clip(ids_tensor([self.batch_size, self.seq_length - 1]		,   self.vocab_size						)		,   3		,   self.vocab_size						)
			lowerCAmelCase  :     str       						= np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)		,   dtype=np.intaa						))		,   -1						)

			lowerCAmelCase  :     List[str]       						= shift_tokens_right(a_		,   1		,   2						)

			lowerCAmelCase  :     Union[str, Any]       						= BlenderbotConfig(
			    vocab_size=self.vocab_size		,   d_model=self.hidden_size		,   encoder_layers=self.num_hidden_layers		,   decoder_layers=self.num_hidden_layers		,   encoder_attention_heads=self.num_attention_heads		,   decoder_attention_heads=self.num_attention_heads		,   encoder_ffn_dim=self.intermediate_size		,   decoder_ffn_dim=self.intermediate_size		,   dropout=self.hidden_dropout_prob		,   attention_dropout=self.attention_probs_dropout_prob		,   max_position_embeddings=self.max_position_embeddings		,   eos_token_id=self.eos_token_id		,   bos_token_id=self.bos_token_id		,   pad_token_id=self.pad_token_id		,   initializer_range=self.initializer_range		,   use_cache=a_		,   )
			lowerCAmelCase  :     Tuple       						= prepare_blenderbot_inputs_dict(a_		,   a_		,   a_						)
			return config, inputs_dict



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase						,		lowerCAmelCase  :     Union[str, Any]       						= self.prepare_config_and_inputs()
			return config, inputs_dict



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_						):
			lowerCAmelCase  :     Any       						= 20
			lowerCAmelCase  :     int       						= model_class_name(a_						)

			lowerCAmelCase  :     Tuple       						= model.encode(inputs_dict["input_ids"]						)

			lowerCAmelCase						,		lowerCAmelCase  :     List[Any]       						= (
			    inputs_dict["decoder_input_ids"],
			    inputs_dict["decoder_attention_mask"],
			)

			lowerCAmelCase  :     Tuple       						= model.init_cache(decoder_input_ids.shape[0]		,   a_		,   a_						)
			lowerCAmelCase  :     Any       						= jnp.ones((decoder_input_ids.shape[0], max_decoder_length)		,   dtype="i4"						)

			lowerCAmelCase  :     Dict       						= jnp.broadcast_to(
			    jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]		,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,   )
			lowerCAmelCase  :     Union[str, Any]       						= model.decode(
			    decoder_input_ids[:, :-1]		,   a_		,   decoder_attention_mask=a_		,   past_key_values=a_		,   decoder_position_ids=a_		,   )

			lowerCAmelCase  :     Tuple       						= jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,   dtype="i4"						)
			lowerCAmelCase  :     Optional[Any]       						= model.decode(
			    decoder_input_ids[:, -1:]		,   a_		,   decoder_attention_mask=a_		,   past_key_values=outputs_cache.past_key_values		,   decoder_position_ids=a_		,   )

			lowerCAmelCase  :     int       						= model.decode(a_		,   a_						)

			lowerCAmelCase  :     List[str]       						= np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
			self.parent.assertTrue(diff < 1e-3		,   msg=F'''Max diff is {diff}'''						)





	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_						):
			lowerCAmelCase  :     Union[str, Any]       						= 20
			lowerCAmelCase  :     Tuple       						= model_class_name(a_						)

			lowerCAmelCase  :     Dict       						= model.encode(inputs_dict["input_ids"]						)

			lowerCAmelCase						,		lowerCAmelCase  :     str       						= (
			    inputs_dict["decoder_input_ids"],
			    inputs_dict["decoder_attention_mask"],
			)

			lowerCAmelCase  :     Any       						= jnp.concatenate(
			    [
			        decoder_attention_mask,
			        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])						),
			    ]		,   axis=-1		,   )

			lowerCAmelCase  :     Union[str, Any]       						= model.init_cache(decoder_input_ids.shape[0]		,   a_		,   a_						)
			lowerCAmelCase  :     Any       						= jnp.broadcast_to(
			    jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]		,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,   )

			lowerCAmelCase  :     Any       						= model.decode(
			    decoder_input_ids[:, :-1]		,   a_		,   decoder_attention_mask=a_		,   past_key_values=a_		,   decoder_position_ids=a_		,   )
			lowerCAmelCase  :     int       						= jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,   dtype="i4"						)
			lowerCAmelCase  :     Any       						= model.decode(
			    decoder_input_ids[:, -1:]		,   a_		,   past_key_values=outputs_cache.past_key_values		,   decoder_attention_mask=a_		,   decoder_position_ids=a_		,   )

			lowerCAmelCase  :     List[str]       						= model.decode(a_		,   a_		,   decoder_attention_mask=a_						)

			lowerCAmelCase  :     int       						= np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
			self.parent.assertTrue(diff < 1e-3		,   msg=F'''Max diff is {diff}'''						)






@require_flax
class 		lowerCamelCase	(   unittest.TestCase							):
	snake_case_   = 99



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     List[Any]       						= np.array(
			    [
			        [71, 82, 18, 33, 46, 91, 2],
			        [68, 34, 26, 58, 30, 82, 2],
			        [5, 97, 17, 39, 94, 40, 2],
			        [76, 83, 94, 25, 70, 78, 2],
			        [87, 59, 41, 35, 48, 66, 2],
			        [55, 13, 16, 58, 5, 2, 1],  # note padding
			        [64, 27, 31, 51, 12, 75, 2],
			        [52, 64, 86, 17, 83, 39, 2],
			        [48, 61, 9, 24, 71, 82, 2],
			        [26, 1, 60, 48, 22, 13, 2],
			        [21, 5, 62, 28, 14, 76, 2],
			        [45, 98, 37, 86, 59, 48, 2],
			        [70, 70, 50, 9, 28, 0, 2],
			    ]		,   dtype=np.intaa		,   )

			lowerCAmelCase  :     Union[str, Any]       						= input_ids.shape[0]
			lowerCAmelCase  :     Dict       						= BlenderbotConfig(
			    vocab_size=self.vocab_size		,   d_model=24		,   encoder_layers=2		,   decoder_layers=2		,   encoder_attention_heads=2		,   decoder_attention_heads=2		,   encoder_ffn_dim=32		,   decoder_ffn_dim=32		,   max_position_embeddings=48		,   eos_token_id=2		,   pad_token_id=1		,   bos_token_id=0		,   )
			return config, input_ids, batch_size



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  :     List[str]       						= self._get_config_and_data()
			lowerCAmelCase  :     Tuple       						= FlaxBlenderbotForConditionalGeneration(a_						)
			lowerCAmelCase  :     str       						= lm_model(input_ids=a_						)
			lowerCAmelCase  :     Tuple       						= (batch_size, input_ids.shape[1], config.vocab_size)
			self.assertEqual(outputs["logits"].shape		,   a_						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Union[str, Any]       						= BlenderbotConfig(
			    vocab_size=self.vocab_size		,   d_model=14		,   encoder_layers=2		,   decoder_layers=2		,   encoder_attention_heads=2		,   decoder_attention_heads=2		,   encoder_ffn_dim=8		,   decoder_ffn_dim=8		,   max_position_embeddings=48		,   )
			lowerCAmelCase  :     int       						= FlaxBlenderbotForConditionalGeneration(a_						)
			lowerCAmelCase  :     Optional[Any]       						= np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]		,   dtype=np.intaa						)
			lowerCAmelCase  :     List[str]       						= np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]		,   dtype=np.intaa						)
			lowerCAmelCase  :     List[str]       						= lm_model(input_ids=a_		,   decoder_input_ids=a_						)
			lowerCAmelCase  :     List[str]       						= (*summary.shape, config.vocab_size)
			self.assertEqual(outputs["logits"].shape		,   a_						)





	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     int       						= np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]		,   dtype=np.intaa						)
			lowerCAmelCase  :     Dict       						= shift_tokens_right(a_		,   1		,   2						)
			lowerCAmelCase  :     Optional[Any]       						= np.equal(a_		,   1						).astype(np.floataa						).sum()
			lowerCAmelCase  :     Any       						= np.equal(a_		,   1						).astype(np.floataa						).sum()
			self.assertEqual(shifted.shape		,   input_ids.shape						)
			self.assertEqual(a_		,   n_pad_before - 1						)
			self.assertTrue(np.equal(shifted[:, 0]		,   2						).all()						)






@require_flax
class 		lowerCamelCase	(   _A   , unittest.TestCase   , _A							):
	snake_case_   = True
	snake_case_   = (
	    (
	        FlaxBlenderbotModel,
	        FlaxBlenderbotForConditionalGeneration,
	    )
	    if is_flax_available()
	    else ()
	)
	snake_case_   = (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Optional[Any]       						= FlaxBlenderbotModelTester(self						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase						,		lowerCAmelCase  :     Optional[int]       						= self.model_tester.prepare_config_and_inputs()
			for model_class in self.all_model_classes:
					self.model_tester.check_use_cache_forward(a_		,   a_		,   a_						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase						,		lowerCAmelCase  :     List[str]       						= self.model_tester.prepare_config_and_inputs()
			for model_class in self.all_model_classes:
					self.model_tester.check_use_cache_forward_with_attn_mask(a_		,   a_		,   a_						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase						,		lowerCAmelCase  :     Optional[Any]       						= self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__						):
							lowerCAmelCase  :     List[str]       						= self._prepare_for_class(a_		,   a_						)
							lowerCAmelCase  :     str       						= model_class(a_						)

							@jax.jit
							def encode_jitted(a_		,   a_=None		,   **a_						):
									return model.encode(input_ids=a_		,   attention_mask=a_						)

							with self.subTest("JIT Enabled"						):
									lowerCAmelCase  :     List[Any]       						= encode_jitted(**a_						).to_tuple()

							with self.subTest("JIT Disabled"						):
									with jax.disable_jit():
											lowerCAmelCase  :     Tuple       						= encode_jitted(**a_						).to_tuple()

							self.assertEqual(len(a_						)		,   len(a_						)						)
							for jitted_output, output in zip(a_		,   a_						):
									self.assertEqual(jitted_output.shape		,   output.shape						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase						,		lowerCAmelCase  :     Optional[int]       						= self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__						):
							lowerCAmelCase  :     int       						= model_class(a_						)
							lowerCAmelCase  :     Optional[int]       						= model.encode(inputs_dict["input_ids"]		,   inputs_dict["attention_mask"]						)

							lowerCAmelCase  :     Optional[Any]       						= {
							    "decoder_input_ids": inputs_dict["decoder_input_ids"],
							    "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
							    "encoder_outputs": encoder_outputs,
							}

							@jax.jit
							def decode_jitted(a_		,   a_		,   a_						):
									return model.decode(
									    decoder_input_ids=a_		,   decoder_attention_mask=a_		,   encoder_outputs=a_		,   )

							with self.subTest("JIT Enabled"						):
									lowerCAmelCase  :     str       						= decode_jitted(**a_						).to_tuple()

							with self.subTest("JIT Disabled"						):
									with jax.disable_jit():
											lowerCAmelCase  :     Optional[Any]       						= decode_jitted(**a_						).to_tuple()

							self.assertEqual(len(a_						)		,   len(a_						)						)
							for jitted_output, output in zip(a_		,   a_						):
									self.assertEqual(jitted_output.shape		,   output.shape						)



	@slow
	def 	_lowerCamelCase     ( self						):
			for model_class_name in self.all_model_classes:
					lowerCAmelCase  :     List[str]       						= model_class_name.from_pretrained("facebook/blenderbot-400M-distill"						)
					# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
					lowerCAmelCase  :     Union[str, Any]       						= np.ones((1, 1)						) * model.config.eos_token_id
					lowerCAmelCase  :     Dict       						= model(a_						)
					self.assertIsNotNone(a_						)





	@unittest.skipUnless(jax_device != "cpu"		,   "3B test too slow on CPU."						)
	@slow
	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Dict       						= {"num_beams": 1, "early_stopping": True, "min_length": 15, "max_length": 25}
			lowerCAmelCase  :     Any       						= {"skip_special_tokens": True, "clean_up_tokenization_spaces": True}

			lowerCAmelCase  :     Optional[int]       						= FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-3B"		,   from_pt=a_						)
			lowerCAmelCase  :     List[str]       						= BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B"						)

			lowerCAmelCase  :     Any       						= ["Sam"]
			lowerCAmelCase  :     int       						= tokenizer(a_		,   return_tensors="jax"						)

			lowerCAmelCase  :     List[Any]       						= model.generate(**a_		,   **a_						)
			lowerCAmelCase  :     Optional[Any]       						= "Sam is a great name. It means \"sun\" in Gaelic."

			lowerCAmelCase  :     Optional[Any]       						= tokenizer.batch_decode(a_		,   **a_						)
			assert generated_txt[0].strip() == tgt_text



